Loss-Of-Function Mutation in the Dioxygenase-Encoding FTO Gene
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Genetic Mutation Analysis of High and Low Igy Chickens by Capture Sequencing
animals Article Genetic Mutation Analysis of High and Low IgY Chickens by Capture Sequencing 1,2, 1,2, 1,3 1,2 1,2 1,2 Qiao Wang y , Fei Wang y, Lu Liu , Qinghe Li , Ranran Liu , Maiqing Zheng , Huanxian Cui 1,2, Jie Wen 1,2 and Guiping Zhao 1,2,4,* 1 Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China; [email protected] (Q.W.); [email protected] (F.W.); [email protected] (L.L.); [email protected] (Q.L.); [email protected] (R.L.); [email protected] (M.Z.); [email protected] (H.C.); [email protected] (J.W.) 2 State Key Laboratory of Animal Nutrition, Beijing 100193, China 3 College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China 4 School of Life Science and Engineering, Foshan University, Foshan 528225, China * Correspondence: [email protected] These authors contributed equally to this work. y Received: 6 March 2019; Accepted: 20 May 2019; Published: 23 May 2019 Simple Summary: Immunoglobulin Y (IgY) is the major antibody produced by hens and it endows their offspring with effective humoral immunity against the pathogens. In previous research, we identified 13 genomic regions that were significantly associated with the serum IgY level or antibody responses to sheep red-blood-cells, but the specific mutations in these regions have not been reported. Therefore, we screened for variations in these regions in White Leghorn and Beijing-You chickens with high and low IgY. Our study identified 35,154 mutations and 829 Indels which were associated with IgY levels in both lines. -
Prolyl Hydroxylase Domain Enzymes: Important Regulators of Cancer Metabolism
Hypoxia Dovepress open access to scientific and medical research Open Access Full Text Article REVIEW Prolyl hydroxylase domain enzymes: important regulators of cancer metabolism Ming Yang1 Abstract: The hypoxia-inducible factor (HIF) prolyl hydroxylase domain enzymes (PHDs) Huizhong Su1 regulate the stability of HIF protein by post-translational hydroxylation of two conserved prolyl Tomoyoshi Soga2 residues in its α subunit in an oxygen-dependent manner. Trans-4-prolyl hydroxylation of HIFα 3 under normal oxygen (O ) availability enables its association with the von Hippel-Lindau (VHL) Kamil R Kranc 2 Patrick J Pollard1 tumor suppressor pVHL E3 ligase complex, leading to the degradation of HIFα via the ubiquitin- proteasome pathway. Due to the obligatory requirement of molecular O2 as a co-substrate, the 1Cancer Biology and Metabolism activity of PHDs is inhibited under hypoxic conditions, resulting in stabilized HIF , which Group, Institute of Genetics and α Molecular Medicine, University of dimerizes with HIFβ and, together with transcriptional co-activators CBP/p300, activates the Edinburgh, Edinburgh, UK; 2Institute transcription of its target genes. As a key molecular regulator of adaptive response to hypoxia, for Advanced Biosciences, Keio For personal use only. University, Mizukami, Tsuruoka, HIF plays important roles in multiple cellular processes and its overexpression has been detected Yamagata, Japan; 3MRC Centre for in various cancers. The HIF1α isoform in particular has a strong impact on cellular metabolism, Regenerative Medicine, University most notably by promoting anaerobic, whilst inhibiting O -dependent, metabolism of glucose. of Edinburgh, Edinburgh, UK 2 The PHD enzymes also seem to have HIF-independent functions and are subject to regulation by factors other than O2, such as by metabolic status, oxidative stress, and abnormal levels of endogenous metabolites (oncometabolites) that have been observed in some types of cancers. -
Identification of Micrornas Controlling Hepatic Mrna Levels for Metabolic
Hicks et al. BMC Genomics (2017) 18:687 DOI 10.1186/s12864-017-4096-5 RESEARCHARTICLE Open Access Identification of microRNAs controlling hepatic mRNA levels for metabolic genes during the metabolic transition from embryonic to posthatch development in the chicken Julie A. Hicks1, Tom E. Porter2 and Hsiao-Ching Liu1* Abstract Background: The transition from embryonic to posthatch development in the chicken represents a massive metabolic switch from primarily lipolytic to primarily lipogenic metabolism. This metabolic switch is essential for the chick to successfully transition from the metabolism of stored egg yolk to the utilization of carbohydrate-based feed. However, regulation of this metabolic switch is not well understood. We hypothesized that microRNAs (miRNAs) play an important role in the metabolic switch that is essential to efficient growth of chickens. We used high-throughput RNA sequencing to characterize expression profiles of mRNA and miRNA in liver during late embryonic and early posthatch development of the chicken. This extensive data set was used to define the contributions of microRNAs to the metabolic switch during development that is critical to growth and nutrient utilization in chickens. Results: We found that expression of over 800 mRNAs and 30 miRNAs was altered in the embryonic liver between embryonic day 18 and posthatch day 3, and many of these differentially expressed mRNAs and miRNAs are associated with metabolic processes. We confirmed the regulation of some of these mRNAs by miRNAs expressed in a reciprocal pattern using luciferase reporter assays. Finally, through the use of yeast one-hybrid screens, we identified several proteins that likely regulate expression of one of these important miRNAs. -
Transcriptome Sequencing and Genome-Wide Association Analyses Reveal Lysosomal Function and Actin Cytoskeleton Remodeling in Schizophrenia and Bipolar Disorder
Molecular Psychiatry (2015) 20, 563–572 © 2015 Macmillan Publishers Limited All rights reserved 1359-4184/15 www.nature.com/mp ORIGINAL ARTICLE Transcriptome sequencing and genome-wide association analyses reveal lysosomal function and actin cytoskeleton remodeling in schizophrenia and bipolar disorder Z Zhao1,6,JXu2,6, J Chen3,6, S Kim4, M Reimers3, S-A Bacanu3,HYu1, C Liu5, J Sun1, Q Wang1, P Jia1,FXu2, Y Zhang2, KS Kendler3, Z Peng2 and X Chen3 Schizophrenia (SCZ) and bipolar disorder (BPD) are severe mental disorders with high heritability. Clinicians have long noticed the similarities of clinic symptoms between these disorders. In recent years, accumulating evidence indicates some shared genetic liabilities. However, what is shared remains elusive. In this study, we conducted whole transcriptome analysis of post-mortem brain tissues (cingulate cortex) from SCZ, BPD and control subjects, and identified differentially expressed genes in these disorders. We found 105 and 153 genes differentially expressed in SCZ and BPD, respectively. By comparing the t-test scores, we found that many of the genes differentially expressed in SCZ and BPD are concordant in their expression level (q ⩽ 0.01, 53 genes; q ⩽ 0.05, 213 genes; q ⩽ 0.1, 885 genes). Using genome-wide association data from the Psychiatric Genomics Consortium, we found that these differentially and concordantly expressed genes were enriched in association signals for both SCZ (Po10 − 7) and BPD (P = 0.029). To our knowledge, this is the first time that a substantially large number of genes show concordant expression and association for both SCZ and BPD. Pathway analyses of these genes indicated that they are involved in the lysosome, Fc gamma receptor-mediated phagocytosis, regulation of actin cytoskeleton pathways, along with several cancer pathways. -
Mouse Aktip Conditional Knockout Project (CRISPR/Cas9)
https://www.alphaknockout.com Mouse Aktip Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Aktip conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Aktip gene (NCBI Reference Sequence: NM_001302267 ; Ensembl: ENSMUSG00000031667 ) is located on Mouse chromosome 8. 11 exons are identified, with the ATG start codon in exon 3 and the TAA stop codon in exon 11 (Transcript: ENSMUST00000120213). Exon 4 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Aktip gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP23-149I24 as template. Cas9, gRNA and targeting vector will be co-injected into fertilized eggs for cKO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Mice homozygous for a hypomorphic allele exhibit impaired growth, skeletal and skin defects, abnormal heart tissue, and sterility. Exon 4 starts from about 4.91% of the coding region. The knockout of Exon 4 will result in frameshift of the gene. The size of intron 3 for 5'-loxP site insertion: 1235 bp, and the size of intron 4 for 3'-loxP site insertion: 2670 bp. The size of effective cKO region: ~706 bp. The cKO region does not have any other known gene. Page 1 of 8 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele gRNA region 5' gRNA region 3' 1 2 3 4 11 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Aktip Homology arm cKO region loxP site Page 2 of 8 https://www.alphaknockout.com Overview of the Dot Plot Window size: 10 bp Forward Reverse Complement Sequence 12 Note: The sequence of homologous arms and cKO region is aligned with itself to determine if there are tandem repeats. -
Probabilistic Models for Collecting, Analyzing, and Modeling Expression Data
Probabilistic Models for Collecting, Analyzing, and Modeling Expression Data Hai-Son Phuoc Le May 2013 CMU-ML-13-101 Probabilistic Models for Collecting, Analyzing, and Modeling Expression Data Hai-Son Phuoc Le May 2013 CMU-ML-13-101 Machine Learning Department School of Computer Science Carnegie Mellon University Thesis Committee Ziv Bar-Joseph, Chair Christopher Langmead Roni Rosenfeld Quaid Morris Submitted in partial fulfillment of the requirements for the Degree of Doctor of Philosophy. Copyright @ 2013 Hai-Son Le This research was sponsored by the National Institutes of Health under grant numbers 5U01HL108642 and 1R01GM085022, the National Science Foundation under grant num- bers DBI0448453 and DBI0965316, and the Pittsburgh Life Sciences Greenhouse. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the U.S. government or any other entity. Keywords: genomics, gene expression, gene regulation, microarray, RNA-Seq, transcriptomics, error correction, comparative genomics, regulatory networks, cross-species, expression database, Gene Expression Omnibus, GEO, orthologs, microRNA, target prediction, Dirichlet Process, Indian Buffet Process, hidden Markov model, immune response, cancer. To Mom and Dad. i Abstract Advances in genomics allow researchers to measure the complete set of transcripts in cells. These transcripts include messenger RNAs (which encode for proteins) and microRNAs, short RNAs that play an important regulatory role in cellular networks. While this data is a great resource for reconstructing the activity of networks in cells, it also presents several computational challenges. These challenges include the data collection stage which often results in incomplete and noisy measurement, developing methods to integrate several experiments within and across species, and designing methods that can use this data to map the interactions and networks that are activated in specific conditions. -
Supplementary Materials
Supplementary materials Supplementary Table S1: MGNC compound library Ingredien Molecule Caco- Mol ID MW AlogP OB (%) BBB DL FASA- HL t Name Name 2 shengdi MOL012254 campesterol 400.8 7.63 37.58 1.34 0.98 0.7 0.21 20.2 shengdi MOL000519 coniferin 314.4 3.16 31.11 0.42 -0.2 0.3 0.27 74.6 beta- shengdi MOL000359 414.8 8.08 36.91 1.32 0.99 0.8 0.23 20.2 sitosterol pachymic shengdi MOL000289 528.9 6.54 33.63 0.1 -0.6 0.8 0 9.27 acid Poricoic acid shengdi MOL000291 484.7 5.64 30.52 -0.08 -0.9 0.8 0 8.67 B Chrysanthem shengdi MOL004492 585 8.24 38.72 0.51 -1 0.6 0.3 17.5 axanthin 20- shengdi MOL011455 Hexadecano 418.6 1.91 32.7 -0.24 -0.4 0.7 0.29 104 ylingenol huanglian MOL001454 berberine 336.4 3.45 36.86 1.24 0.57 0.8 0.19 6.57 huanglian MOL013352 Obacunone 454.6 2.68 43.29 0.01 -0.4 0.8 0.31 -13 huanglian MOL002894 berberrubine 322.4 3.2 35.74 1.07 0.17 0.7 0.24 6.46 huanglian MOL002897 epiberberine 336.4 3.45 43.09 1.17 0.4 0.8 0.19 6.1 huanglian MOL002903 (R)-Canadine 339.4 3.4 55.37 1.04 0.57 0.8 0.2 6.41 huanglian MOL002904 Berlambine 351.4 2.49 36.68 0.97 0.17 0.8 0.28 7.33 Corchorosid huanglian MOL002907 404.6 1.34 105 -0.91 -1.3 0.8 0.29 6.68 e A_qt Magnogrand huanglian MOL000622 266.4 1.18 63.71 0.02 -0.2 0.2 0.3 3.17 iolide huanglian MOL000762 Palmidin A 510.5 4.52 35.36 -0.38 -1.5 0.7 0.39 33.2 huanglian MOL000785 palmatine 352.4 3.65 64.6 1.33 0.37 0.7 0.13 2.25 huanglian MOL000098 quercetin 302.3 1.5 46.43 0.05 -0.8 0.3 0.38 14.4 huanglian MOL001458 coptisine 320.3 3.25 30.67 1.21 0.32 0.9 0.26 9.33 huanglian MOL002668 Worenine -
FTS (AKTIP) (NM 022476) Human Untagged Clone Product Data
OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for SC112492 FTS (AKTIP) (NM_022476) Human Untagged Clone Product data: Product Type: Expression Plasmids Product Name: FTS (AKTIP) (NM_022476) Human Untagged Clone Tag: Tag Free Symbol: AKTIP Synonyms: FT1; FTS Vector: pCMV6-XL5 E. coli Selection: Ampicillin (100 ug/mL) Cell Selection: None Fully Sequenced ORF: >NCBI ORF sequence for NM_022476, the custom clone sequence may differ by one or more nucleotides ATGAACCCTTTCTGGAGCATGTCTACAAGCTCTGTACGCAAACGATCTGAAGGTGAAGAGAAGACATTAA CAGGGGACGTGAAAACCAGTCCTCCACGAACTGCACCAAAGAAACAGCTGCCTTCTATTCCCAAAAATGC TTTGCCCATAACTAAGCCTACATCTCCTGCCCCAGCAGCACAGTCAACAAATGGCACGCATGCGTCCTAT GGACCCTTCTACCTGGAATACTCTCTTCTTGCAGAATTTACCTTGGTTGTGAAGCAGAAGCTACCAGGCG TCTATGTGCAGCCATCTTATCGCTCTGCATTAATGTGGTTTGGAGTAATATTCATACGGCATGGACTTTA CCAAGATGGCGTATTTAAGTTTACAGTTTACATCCCTGATAACTATCCAGATGGTGACTGTCCACGCTTG GTGTTCGATATTCCTGTCTTTCACCCGCTAGTTGATCCCACCTCAGGTGAGCTGGATGTGAAGAGAGCAT TTGCAAAATGGAGGCGGAACCATAATCATATTTGGCAGGTATTAATGTATGCAAGGAGAGTTTTCTACAA GATTGATACAGCAAGCCCCCTGAACCCAGAGGCTGCAGTACTGTATGAAAAAGATATTCAGCTTTTTAAA AGTAAAGTTGTTGACAGTGTTAAGGTGTGCACTGCTCGTTTGTTTGACCAACCTAAAATAGAAGACCCCT ATGCAATTAGCTTTTCTCCATGGAATCCTTCTGTACATGATGAAGCCAGAGAAAAGATGCTGACTCAGAA AAAGCCTGAAGAACAGCACAATAAAAGTGTTCATGTTGCTGGCCTGTCATGGGTAAAGCCTGGCTCAGTA CAGCCTTTCAGTAAAGAAGAGAAAACAGTGGCGACTTAA This product is to be used for laboratory only. Not for -
Genomics of Body Fat Distribution
Journal of Genetics (2021)100:32 Ó Indian Academy of Sciences https://doi.org/10.1007/s12041-021-01281-7 (0123456789().,-volV)(0123456789().,-volV) REVIEW ARTICLE Genomics of body fat distribution SIMMI SAINI1, GAGANDEEP KAUR WALIA2, MOHINDER PAL SACHDEVA1 and VIPIN GUPTA1* 1Department of Anthropology, University of Delhi, Delhi 110 007, India 2Public Health Foundation of India, Gurugram 122 002, India *For correspondence. E-mail: [email protected]. Received 1 May 2020; revised 10 November 2020; accepted 4 January 2021 Abstract. Central obesity and body fat distribution measured by waist circumference (WC) and waist hip ratio (WHR) are good predictors of cardio metabolic adversities independent of overall adiposity. There are substantial evidence that body fat distribution is controlled by genetic factors. Even after accounting for body mass index (BMI), individual variation in body fat distribution is heritable, with estimates ranging from 31–76%. Individuals genetically predisposed to store more fat in visceral depots are at higher risk of developing metabolic complications. Several linkage and genomewide association studies (GWAS) for measures of body fat distribution uncovered numerous loci harbouring genes potentially regulating body fat distribution. Additionally, genes with fat depot specific expression patterns (especially, subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT)) have provided plausible candidate genes involved in body fat regulation. Further, sexual dimorphism have revealed a remarkable heterogeneity in the genetic regulation of body fat distribution. More than hundred loci have been identified through GWAS, displaying more pronounced effect in females than males, suggesting that both sexes share potentially different biological architecture in traits related to body fat distribution. -
Aprioritized Panel of Candidate Genes
In: Advances in Genetics Research. Volume 9 ISBN: 978-1- 62081-466-6 Editor: Kevin V. Urbano © 2012 Nova Science Publishers, Inc No part of this digital document may be reproduced, stored in a retrieval system or transmitted commercially in any form or by any means. The publisher has taken reasonable care in the preparation of this digital document, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained herein. This digital document is sold with the clear understanding that the publisher is not engaged in rendering legal, medical or any other professional services. Chapter 1 A PRIORITIZED PANEL OF CANDIDATE GENES TO BE EXPLORED FOR ASSOCIATIONS WITH THE BLOOD PRESSURE RESPONSE TO EXERCISE Garrett I. Ash,1,Amanda L. Augeri,2John D. Eicher,3 Michael L. Bruneau, Jr.1 and Linda S. Pescatello1 1University of Connecticut, Storrs, CT, US 2Hartford Hospital, Hartford, CT, US 3Yale University School of Medicine, New Haven, CT, US ABSTRACT Introduction. Identifying genetic variants associated with the blood pressure (BP) response to exercise is hindered by the lack of standard criteria for candidate gene selection, underpowered samples, and a limited number of conclusive whole genome studies. We developed a prioritized panel of candidate genetic variants that may increase the likelihood of finding genotype associations with the BP response to exercise when used alone or in conjunction with high throughput genotyping studies. Methods. We searched for candidate genetic variants meeting preestablished criteria using PubMed and published systematic reviews. -
The Relationship Between Selected CNR1, MC4R, LEP, FTO and VDR
Journal of Clinical Medicine Article The Relationship between Selected CNR1, MC4R, LEP, FTO and VDR Gene Polymorphisms and Several Basic Toxicological Parameters Among Persons Occupationally Exposed to Arsenic, Cadmium and Lead Tomasz Matys 1, Anna Szyma ´nska-Chabowska 1, Katarzyna Bogunia-Kubik 2, Beata Smyk 1, Małgorzata Kami ´nska 2, Grzegorz Mazur 1 , Rafał Por˛eba 1 and Paweł Ga´c 3,* 1 Department of Internal Medicine, Occupational Diseases, Hypertension and Clinical Oncology, Wroclaw Medical University, Borowska 213, PL 50-556 Wroclaw, Poland; [email protected] (T.M.); [email protected] (A.S.-C.); [email protected] (B.S.); [email protected] (G.M.); [email protected] (R.P.) 2 Laboratory of Clinical Immunogenetics and Pharmacogenetics, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Weigla 12, PL 53-114 Wroclaw, Poland; [email protected] (K.B.-K.); [email protected] (M.K.) 3 Department of Hygiene, Wroclaw Medical University, Mikulicza-Radeckiego 7, PL 50-368 Wroclaw, Poland * Correspondence: [email protected]; Tel.: +48-71-784-1502; Fax.: +48-71-784-1503 Received: 24 February 2020; Accepted: 6 April 2020; Published: 7 April 2020 Abstract: The purpose of this work was to assess the influence of selected CNR1, MC4R, LEP, FTO and VDR FOKI gene polymorphisms on blood and urine concentration markers of lead, cadmium and arsenic in a population directly exposed to these metals. Eighty-five people exposed to lead, arsenic and cadmium were qualified to take part in the study. Standard urine samples and 25 mL of venous blood from each worker were collected to assay basic laboratory and toxicological markers as well as selected single nucleotide polymorphisms (SNPs) within CNR1—cannabinoid receptor 1 gene (rs806368, rs806381, rs1049353, rs12720071), MC4R—melanocortin 4 receptor gene (rs17782313), LEP—leptin promoter gene (rs7799039), FTO—alpha-ketoglutarate-dependent dioxygenase gene (rs9939609) and VDR—vitamin D receptor (rs10735810) genes. -
Lack of Association Between Genetic Polymorphism of FTO
J Pediatr (Rio J). 2016;92(5):521---527 www.jped.com.br ORIGINAL ARTICLE Lack of association between genetic polymorphism of FTO, AKT1 and AKTIP in childhood overweight ଝ and obesity a,1 a,∗,1 Patrícia de Araújo Pereira , António Marcos Alvim-Soares Jr. , d c Valéria Cristina Sandrim , Carla Márcia Moreira Lanna , b b Débora Cristine Souza-Costa , Vanessa de Almeida Belo , a b Jonas Jardim de Paula , José Eduardo Tanus-Santos , a a Marco Aurélio Romano-Silva , Débora Marques de Miranda a Universidade Federal de Minas Gerais (UFMG), INCT de Medicina Molecular, Belo Horizonte, MG, Brazil b Universidade de São Paulo (USP), Faculdade de Medicina de Ribeirão Preto (FMRP), Departamento de Farmacologia, Ribeirão Preto, SP, Brazil c Universidade Federal de Juiz de Fora (UFJF), Instituto de Ciências Biológicas, Departamento de Fisiologia, Juiz de Fora, MG, Brazil d Universidade Estadual Paulista (UNESP), São Paulo, SP, Brazil Received 15 July 2015; accepted 15 December 2015 Available online 21 June 2016 KEYWORDS Abstract Single-nucleotide Objective: Obesity is a chronic disease caused by both environmental and genetic factors. polymorphisms; Epidemiological studies have documented that increased energy intake and sedentary lifestyle, as well as a genetic contribution, are forces behind the obesity epidemic. Knowledge about the Childhood obesity; interaction between genetic and environmental components can facilitate the choice of the Fat mass and obesity associated; most effective and specific measures for the prevention of obesity. The aim of this study was Gene; to assess the association between the FTO, AKT1, and AKTIP genes and childhood obesity and AKT1; insulin resistance. AKTIP Methods: This was a case---control study in which SNPs in the FTO (rs99396096), AKT1, and AKTIP genes were genotyped in groups of controls and obese/overweight children.